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variability and the predictability of mechanistic CH4 models. We aim to fill the knowledge gap in the project “A holistic view of Methane turnover in northern Wetlands by Novel isotopic approach (MeWeN
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tools, including 4D point cloud modeling and state-of-the-art machine learning and deep learning techniques (such as generative adversarial networks), with empirical fieldwork in Norwegian glacier
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models using sophisticate genetic tools, in vivo time-lapse imaging and multi-omics methods to decipher the underpinning mechanisms of regeneration. Our findings provide new targetable mechanisms
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developed will be based on pseudonymization, anonymization, and synthetic data generation. Using real health data as a source of information, we aim to create test datasets and statistical models
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Researcher will strengthen the group’s work on developing a transnational comparative model. A potential focus of their research either on the so-called ‘second-world’ literatures or the European literary
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the entire population. The project utilises advanced statistical methods such as multilevel models (mixed models), fixed-effects models, cluster analysis, and sequence analysis. The selected researcher is
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(FIMM) , University of Helsinki, is currently seeking a highly-motivated postdoctoral researcher to join our interdisciplinary team. Project overview This project aims to develop machine learning models
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. Integrate environmental, spatial, and social data into digital twin models for scenario testing and policy simulation. Adapt co-design methods to local contexts in demonstrator sites (Portugal, Sweden, Italy
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, programming, Linux, data, and infrastructure perspective: short-term projects helping researchers with specific tasks, so that the researchers gain competence to work independently. Provide good role models
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mental health and computational social science, using large-scale social media analysis, smartphone-based sensing, and agent-based modeling. Combining macro-level patterns with micro-level behavioral data